00:01
So for this problem, it's a little complicated the way that you would do this without r.
00:06
Particularly, basically, we have that the sample size here, n equals 40, is sufficiently large that with a lot of books, they will say that you can approximate the p -value just fine using the z -distribution.
00:21
That is a sample size such that it's going to be pretty difficult to find that on a t -distribution table, table, especially if you look at a typical t distribution table like what i have here, what you end up getting is sort of a range of values at best.
00:38
So that being said, if you are looking to find basically what value would be closest using the z distribution should get you most the way there.
00:47
So what we do here, let's see, we know that she thinks that the average age is now lower, so the p -value is going to be the probability or the proportion of the standard normal distribution to the left of the z -score corresponding to our observations, which would be the sample mean minus the null hypothesized mean divided by the sample standard deviation or really it's sigma population standard deviation but we're approximating it with our sample standard deviation divided by the square root of n.
01:16
So one second here, leftovers from a previous problem.
01:20
Looking at the values that we have, we have a sample mean age of 36, null hypothesized mean or the previous mean 10 years ago is 35, the standard deviation is 6, and it was a sample of 40 residents.
01:34
So it's going to be approximately the proportion of the standard normal distribution to the left of 1 .05.
01:42
If we were looking at the closest that we can get on a t -table, technically it should be for 39 degrees of freedom.
01:50
We can see immediately 1 point...
01:52
Oh wait a second...
01:53
Wow it actually is on the table! 1 .05...